Many image processing and robotics applications involve image segmentation. Image segmentation typically creates a label for each pixel of an image frame to define that pixel's membership class. For example, in an Advanced Driver Assistance System (ADAS), pixels might be classified and labeled as one of “road,” “car,” “pedestrian,” or “sign.” Image segmentation is also applicable to tasks such as obstacle avoidance, path/trajectory planning, robotic vision, and object identification class correction. The segmentation problem is relatively complex and computationally intensive because the characteristics of an entire image frame generally need to be learned, for example using a neural network, based on statistics calculated from all or most of the pixels in that image. The segmentation problem becomes even more difficult in video applications where many such image frames need to be processed.
Although the following Detailed Description will proceed with reference being made to illustrative embodiments, many alternatives, modifications, and variations thereof will be apparent in light of this disclosure.